Introduction to Statistical Genetic Data Analysis

A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics.

Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required.

The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website.

Melinda C. Mills is Professor at the University of Oxford and Nuffield College, where she is also Director of the Leverhulme Centre for Demographic Science.

Nicola Barban is Associate Professor at the Institute for Social and Economic Research at the University of Essex.

Felix Tropf is Assistant Professor at École Nationale de la Statistique et de L'administration Économique (ENSAE) and Center for Research in Economics and Statistics (CREST), Paris.